Evolving neural arrays: a new mechanism for learning complex action sequences
Incremental evolution has proved to be an extremely useful mechanism in complex actions sequence learning. Its performance is based on the decomposition of the original problem into increasingly complex stages whose learning is carried out sequentially, starting from the simplest stage and thus incr...
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| Formato: | Articulo |
| Lenguaje: | Inglés |
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2017
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/103776 http://www.clei.org/cleiej/index.php/cleiej/article/view/348 |
| Aporte de: |
| id |
I19-R120-10915-103776 |
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dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas Evolving neural nets Learning Complex actions sequence learning Incremental evolution Genetic algorithms |
| spellingShingle |
Ciencias Informáticas Evolving neural nets Learning Complex actions sequence learning Incremental evolution Genetic algorithms Corbalán, Leonardo César Lanzarini, Laura Cristina Evolving neural arrays: a new mechanism for learning complex action sequences |
| topic_facet |
Ciencias Informáticas Evolving neural nets Learning Complex actions sequence learning Incremental evolution Genetic algorithms |
| description |
Incremental evolution has proved to be an extremely useful mechanism in complex actions sequence learning. Its performance is based on the decomposition of the original problem into increasingly complex stages whose learning is carried out sequentially, starting from the simplest stage and thus increasing its generality and difficulty.
The present work proposes neural array applications as a novel mechanism for complex actions sequence learning. Each array is composed by several neural nets obtained by means of an evolving process allowing them to acquire various degrees of specialization. Neural nets constituting the same array are organized so that, in each assessment, there is only one in charge of its response.
The proposed strategy is applied to problems presented by obstacle evasion and target reaching as a means to show the capability of this proposal to solve complex problems. The measurements carried out show the superiority of evolving neural arrays over traditional neuroevolving methods that handle neural network populations – SANE is being particularly used as a comparative reference due to its high performance.
Neural array capability to recover from previous defective evolving stages has been tested, evincing highly plausible final successful outcomes – even in those adverse cases.
Finally, conclusions are presented as well as some future lines of work. |
| format |
Articulo Articulo |
| author |
Corbalán, Leonardo César Lanzarini, Laura Cristina |
| author_facet |
Corbalán, Leonardo César Lanzarini, Laura Cristina |
| author_sort |
Corbalán, Leonardo César |
| title |
Evolving neural arrays: a new mechanism for learning complex action sequences |
| title_short |
Evolving neural arrays: a new mechanism for learning complex action sequences |
| title_full |
Evolving neural arrays: a new mechanism for learning complex action sequences |
| title_fullStr |
Evolving neural arrays: a new mechanism for learning complex action sequences |
| title_full_unstemmed |
Evolving neural arrays: a new mechanism for learning complex action sequences |
| title_sort |
evolving neural arrays: a new mechanism for learning complex action sequences |
| publishDate |
2017 |
| url |
http://sedici.unlp.edu.ar/handle/10915/103776 http://www.clei.org/cleiej/index.php/cleiej/article/view/348 |
| work_keys_str_mv |
AT corbalanleonardocesar evolvingneuralarraysanewmechanismforlearningcomplexactionsequences AT lanzarinilauracristina evolvingneuralarraysanewmechanismforlearningcomplexactionsequences |
| bdutipo_str |
Repositorios |
| _version_ |
1764820441150521344 |